First, data were obtained from the Brazilian Ministry of Health via the integrated data bank RESP (Registro de Eventos em Saúde Pública/Public Health Events Registry) to quantify the total amount of cases of microcephaly that were confirmed and reported still under examination between 2015 and 2018. This amount was standardized by 1000 inhabitants. The total number of patients hospitalized by any type of undernutrition between 2009 and 2018, normalized by 1000 inhabitants, were extracted from the Brazilian Ministry of Health public data bank CID-10 (Classificação Estatística Internacional de Doenças e Problemas Relacionados à Saúde/International Statistical Classification of Diseases and Related Health Problems) available at www.datasus.gov.br/cid10/V2008/cid10.htm. Undernutrition was defined by the health agents as cases diagnosed and satisfying at least one of the following conditions: kwashiorkor, nutritional marasmus, severe calorie-protein undernutrition, moderate to mild calorie-protein undernutrition, growth retardation due to calorie-protein undernutrition, and nonspecified calorie-protein undernutrition. For the definition of these categories, which were codified as E40-46 in the CID-10 public bank, the criteria defined by the World Health Organization were applied. A decade’s worth of data regarding hospitalization due to undernutrition was taken to have a more consistent description of this variable. The coefficient of variation for the raw number of patients hospitalized by undernutrition over these years ranged between 2.00 for the state of Sergipe and 11.15 for the state of Espirito Santo. To avoid sampling bias, municipals with less than 20,000 inhabitants were excluded. Values for both variables (microcephaly and undernourishment) were obtained for all tropical states in the Northeast, North, Central-West, and Southeast regions. A Pearson correlation analysis was performed, and a simple linear regression was also carried out to assess the association between both variables. After obtaining a P value under the assumptions of a parametric test for this correlation, a simulation approach was used to estimate the significance of the correlation using a Monte Carlo permutation analysis. For this purpose, the values from our original sample were randomly correlated (999 times), and the frequency of each r coefficient was registered and illustrated in fig. S1. Then, by comparing our original r coefficient in the simulated distribution, it was possible to detect how probable our correlation to occur by chance is, and an adjusted P value was derived.

In addition, a survey on a sample of mothers who have children with CZS was performed in the Northeastern state of Ceará. We interviewed 83 affected mothers, and a diet frequency questionnaire was completed, in which we asked the number of times they ate different categories of foods per week. Data that support this part of the study are available on request from the corresponding author (P.P.G.). Ethical approval was obtained for data collection in protocol 2385592 from the Federal University of Ceará. On this basis, we quantified the amount of daily protein consumption by multiplying the daily frequency of consumption of each food by the protein content of this food in a standard portion. The total daily protein intake was obtained by summing these values for the consumed foods, as described by Rogers and Emmett (39). According to current recommendations, the average consumption of protein during pregnancy should be above ~60 g/day (www.nrv.gov.au/nutrients/protein). We subsequently quantified the number of cases where consumption was either below or above 61 g/day to have an estimation of the portion of the sample affected by low protein intake.

The pattern of protein intake obtained from the interviewed mothers was compared with a reference for the state of Ceará and the whole Northeast region. For this purpose, we used the “Survey of familiar incomes 2008-2009” (Pesquisa de orçamento familiares 2008-2009) database of the IBGE and depicted the average values for Ceará and the Northeast together with the histogram of our sample. For Ceará state, the average value of protein intake was estimated from the data on the frequency of consumption of different foods, while the value of daily protein intake for the entire Northeastern region is available in the IBGE data bank by age and sex. In this case, the protein intake of women between 19 and 59 years was taken as a reference. In addition, we statistically compared the means of our sample and reference of Ceará for the variable daily protein intake using a t test. Similarly, we estimated the relative risk of CZS in relation to the protein intake level. Since our condition (CZS) had low frequency in the population, we carried out the analyses under “the rare disease assumption” and estimated the odd ratio (40). Given that the frequency of CZS is rare in the population, reference data for Ceará can be considered as representative of the protein intake of a non-CZS group.

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